Research Scientist
Confirmed on the employer's own hiring board on Nov 6, 2026. First seen by Alion on Nov 6, 2026.
About Tokka Labs
Tokka Labs is a Singapore-based proprietary trading firm and market maker in digital assets and tokenised real-world assets, bringing traditional instruments into markets that never close.
Our team has built and run trading businesses at top-tier global firms, and we bring that rigour to on-chain markets, where continuous trading and evolving market structure create problems with no existing playbook.
We build our systems and strategies in-house and stay deliberately lean and flat. Traders, researchers and engineers work side by side, and good ideas reach live trading quickly. With AI and agentic tooling central to how we work, each person has the leverage of a much larger team and focuses on the hardest problems. We look for curiosity and ownership, and give strong people the room to act on their ideas and see their impact directly.
The role
We're hiring a research scientist to build AI systems that learn from their own research and improve over time, and to apply them to real problems across the firm.
We believe the most durable edge comes from knowledge that compounds. We're building AI-driven research systems that help our teams find and validate improvements faster. Your job is to make them general, reliable and cumulative, and bring them to new problems across the firm.
You'll report directly to the head of research and work with our trading and engineering teams. The core challenges are open research problems: getting automated research to build on what it has learned, and making sure the improvements it finds are real.
We care more about strong machine learning and evaluation judgment than finance knowledge.
What you'll do
- Research: Find where current frontier models fall short as autonomous researchers in our systems, and turn those limits into research directions.
- Experiments: Design and run experiments, and turn the results into new methods.
- Knowledge: Design how our systems capture proven methods and tools from their work, and reuse them so each new problem is solved faster and at lower cost.
- Evaluation: Build evaluations that tell real improvement from noise, and what it costs in time and tokens, across ML, software engineering and strategy problems.
- Generalize: Turn our research systems into reusable tools that new problems can adopt quickly.
- Automation: Reduce the manual work of bringing a new problem on board.
- Operations: Keep the systems reliable, observable and cost-effective.
- Communication: Explain what worked, what didn't and why.
What we're looking for
- Research track record: Publications or equivalent research in ML, optimization, statistics or a related field, and the ability to turn papers into ideas worth testing.
- Agents and learning systems: Hands-on work with frontier LLM agents, reinforcement learning or systems that learn from experience, including a clear sense of where they break.
- Rigor: You design careful experiments and catch your own overfitting or misleading results.
- Applied ML: Hands-on experience training and evaluating models on real data, ideally shipped to production.
- Coding: You can turn ideas into working production code.
- Background: A PhD with 1+ year of experience, or a Master's with 3+ years, in Computer Science, Statistics, Electrical Engineering, Applied Mathematics, Physics or a related quantitative field, or equivalent research experience.
- Clear communication: You can explain a result, and its uncertainty, to people who weren't in the weeds.
- Ownership and resilience: You'd rather own a problem end to end, across team boundaries, than hand it off. You untangle ambiguity until you understand the real problem, and you stay resilient when experiments fail or plans change.
- Pace and adaptability: You thrive in fast-paced environments that require you to think on your feet and drive solutions within short time frames. You juggle multiple priorities, pivot quickly when they change, and do your best work when they shift.
- Self-improving systems: Experience with automated scientific discovery, open-ended search or self-improving agents, is nice to have.
- Time-series data: Experience modeling noisy, nonstationary time series, is nice to have.
- Modeling range: Hands-on PyTorch, including transformers or other sequence models.
- Markets: Exposure to market microstructure, crypto, DeFi or arbitrage. Helpful, but not expected.
Why join us
- Build with real backing. Leadership is investing heavily in AI and agentic tooling as core strategy, not a side project.
- Work alongside people who have built and run trading businesses at top-tier global firms.
- Real stakes. Your code runs on the critical path, and its impact shows up directly in P&L.
Tokka Labs does not accept uns 2olicited resumes.
Any form of candidate introduction shared without the prior approval of the talent acquisition team will be deemed free to contact by Tokka Labs without restriction or liability. No placement fee of any kind will be paid in the event the identified candidate is hired by Tokka Labs.
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